Finally made it! It’s been a long ride, but the first real STT guide for kids’ voices on edge devices is here. Check it out on Gumroad if you're curious!

the link for the book mlvanguards.gumroad.com/l/fine-tunin... (it's free)

Without observability, agents are just black boxes making guesses.
With the right tools, they become transparent, testable, improvable systems.

Agentic systems are only as useful as their debug-ability.

MLOps is more than just tools.

Reproducibility, scalability, and observability are a must. Just setting up Kubeflow or MLflow doesn’t make you an MLOps expert.

Raw audio is unpredictable.
Training is expensive.
Inference is a balancing act.
Deployment is… well, never as simple as pip install.

Everyone’s hyped about LLMs and edge deployments, but few talk about what it actually takes to it to production.

So we wrote the guide we wish we had.

Real MLOps doesn’t happen in 1 day or 1 week. Knowing how to use different tools doesn’t make you an MLOps expert.

It takes time to understand the whole process, and it takes time to learn how to have arguments to convince the management, the CEO, or other stakeholders.

🚨 Alert: This article is dangerously true!

mlvanguards.substack.com/p/the-malpra...

How does GraphRAG work? What are its advantages over other RAG?

Let's return to Michael Hunger's blog post in which he demonstrates its practical application using a hashtag#Neo4j example!

Happy weekend reading!

bit.ly/4f8wLVp

hashtag#knowledgegraphs

In less than 24 hours, we turned 5K messy files (from @kaggle.com) into a clean dataset of ~3.9K audio+transcription pairs.

Here’s how:
- Resample & filter (standardize to 16kHz, cut long/empty clips)
- Auto-transcribe with SpeechBrain (ran it on CPU — I'm GPU poor 😅)

1/2

In the last 2 years, prompt engineering has been treated as an afterthought, a means to an end.

But in reality, a prompt is the most crucial hyperparameter of any GenAI system. Its design can make or break the output quality, much like tuning a model's parameters determines its performance.

We're launching an ebook on 28th this week 😳

Regular speech-to-text tech struggles with kids’ voices since they’re higher-pitched and less predictable.

We worked on that by creating a smaller, more accurate model that works well with children’s speech, even in noisy or low-power settings.

The principles of an indexing pipeline:

1/7

Not all PDFs are created equally. Some PDFs are beautifully structured with clean text, while others are chaotic with dense layouts, tables, or images.

Ignoring these differences means risking ineffective indexing and poor search retrieval.

It's the evals, stupid

AI can pretty much do anything, but it lacks that human creativity. Do you prefer quick tasks done or creativity?
#AI #Question

"prompt engineering" is just fancy copy-pasting at this point

people tweaking prompts like they're adjusting a car mirror, thinking it'll make them drive better

you’re optimizing nothing, you’re just guessing

Hot take: Meta’s working on a brain-reading tech that turns thoughts into text. No surgery, just a headset. It’s got typos (32% errors) but skilled typists did way better. It's wild to imagine this helping folks who can’t speak or type. Would you trust a computer with your thoughts?

Trend keeps moving with all these new tools making it tough to keep track. The question is ChatGPT or Perplexity for better searching?

Indexing messy PDFs? Try these:

LayoutAnalyzer for messy formatting.
TextDensityAnalyzer to tell text from images.
VisualElementAnalyzer for charts/diagrams.
TableDetector to grab tables.

Convert them to images and use Vision Models like ColPali/ColQwen2.

Are you stuck in the whirlpool of AI trends?

Building production-grade AI isn’t about chasing buzzwords — it’s about combining engineering knowledge with practical AI to deliver systems that actually work, scale, and drive real-world impact.

We got you covered at mlvanguards.substack.com